CaBuAr: California Burned Areas dataset for delineation
- URL: http://arxiv.org/abs/2401.11519v1
- Date: Sun, 21 Jan 2024 15:22:15 GMT
- Title: CaBuAr: California Burned Areas dataset for delineation
- Authors: Daniele Rege Cambrin, Luca Colomba, Paolo Garza
- Abstract summary: This paper introduces a novel open dataset that tackles the burned area delineation problem.
It consists of pre- and post-fire Sentinel-2 L2A acquisitions of California forest fires that took place starting in 2015.
In conjunction with the dataset, we release three different baselines based on spectral indexes analyses, SegFormer, and U-Net models.
- Score: 5.432724320036955
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Forest wildfires represent one of the catastrophic events that, over the last
decades, caused huge environmental and humanitarian damages. In addition to a
significant amount of carbon dioxide emission, they are a source of risk to
society in both short-term (e.g., temporary city evacuation due to fire) and
long-term (e.g., higher risks of landslides) cases. Consequently, the
availability of tools to support local authorities in automatically identifying
burned areas plays an important role in the continuous monitoring requirement
to alleviate the aftereffects of such catastrophic events. The great
availability of satellite acquisitions coupled with computer vision techniques
represents an important step in developing such tools. This paper introduces a
novel open dataset that tackles the burned area delineation problem, a binary
segmentation problem applied to satellite imagery. The presented resource
consists of pre- and post-fire Sentinel-2 L2A acquisitions of California forest
fires that took place starting in 2015. Raster annotations were generated from
the data released by California's Department of Forestry and Fire Protection.
Moreover, in conjunction with the dataset, we release three different baselines
based on spectral indexes analyses, SegFormer, and U-Net models.
Related papers
- Decision support system for Forest fire management using Ontology with Big Data and LLMs [0.8668211481067458]
Fire weather indices, which assess wildfire risk and predict resource demands, are vital.
With the rise of sensor networks in fields like healthcare and environmental monitoring, semantic sensor networks are increasingly used to gather climatic data.
This paper discusses using Apache Spark for early forest fire detection, enhancing fire risk prediction with meteorological and geographical data.
arXiv Detail & Related papers (2024-05-18T17:30:30Z) - Natural Disaster Analysis using Satellite Imagery and Social-Media Data
for Emergency Response Situations [0.0]
This research has been divided into two stages, namely, satellite image analysis and twitter data analysis.
The first stage involves pre and post disaster satellite image analysis of the location.
The second stage focuses on mapping the region with essential information about the disaster situation.
arXiv Detail & Related papers (2023-11-16T15:01:26Z) - FLOGA: A machine learning ready dataset, a benchmark and a novel deep
learning model for burnt area mapping with Sentinel-2 [41.28284355136163]
Wildfires pose significant threats to human and animal lives, ecosystems, and socio-economic stability.
In this work, we create and introduce a machine-learning ready dataset we name FLOGA (Forest wiLdfire Observations for the Greek Area)
This dataset is unique as it comprises of satellite imagery acquired before and after a wildfire event.
We use FLOGA to provide a thorough comparison of multiple Machine Learning and Deep Learning algorithms for the automatic extraction of burnt areas.
arXiv Detail & Related papers (2023-11-06T18:42:05Z) - Rapid Deforestation and Burned Area Detection using Deep Multimodal
Learning on Satellite Imagery [3.8073142980733]
Deforestation estimation and fire detection in the Amazon forest poses a significant challenge due to the vast size of the area.
multimodal satellite imagery and remote sensing offer a promising solution for estimating deforestation and detecting wildfire in the Amazonia region.
This research paper introduces a new curated dataset and a deep learning-based approach to solve these problems using convolutional neural networks (CNNs) and comprehensive data processing techniques.
arXiv Detail & Related papers (2023-07-10T21:49:30Z) - SensatUrban: Learning Semantics from Urban-Scale Photogrammetric Point
Clouds [52.624157840253204]
We introduce SensatUrban, an urban-scale UAV photogrammetry point cloud dataset consisting of nearly three billion points collected from three UK cities, covering 7.6 km2.
Each point in the dataset has been labelled with fine-grained semantic annotations, resulting in a dataset that is three times the size of the previous existing largest photogrammetric point cloud dataset.
arXiv Detail & Related papers (2022-01-12T14:48:11Z) - Disaster mapping from satellites: damage detection with crowdsourced
point labels [4.511561231517167]
High-resolution satellite imagery available immediately after disaster events is crucial for response planning.
Damage mapping at this scale would require hundreds of expert person-hours.
Crowdsourcing and recent advances in deep learning reduces the effort needed to just a few hours in real time.
arXiv Detail & Related papers (2021-11-05T18:32:22Z) - From Static to Dynamic Prediction: Wildfire Risk Assessment Based on
Multiple Environmental Factors [69.9674326582747]
Wildfire is one of the biggest disasters that frequently occurs on the west coast of the United States.
We propose static and dynamic prediction models to analyze and assess the areas with high wildfire risks in California.
arXiv Detail & Related papers (2021-03-14T17:56:17Z) - STCNet: Spatio-Temporal Cross Network for Industrial Smoke Detection [52.648906951532155]
We propose a novel Spatio-Temporal Cross Network (STCNet) to recognize industrial smoke emissions.
The proposed STCNet involves a spatial to extract texture features and a temporal pathway to capture smoke motion information.
We show that our STCNet achieves clear improvements on the challenging RISE industrial smoke detection dataset against the best competitors by 6.2%.
arXiv Detail & Related papers (2020-11-10T02:28:47Z) - MSNet: A Multilevel Instance Segmentation Network for Natural Disaster
Damage Assessment in Aerial Videos [74.22132693931145]
We study the problem of efficiently assessing building damage after natural disasters like hurricanes, floods or fires.
The first contribution is a new dataset, consisting of user-generated aerial videos from social media with annotations of instance-level building damage masks.
The second contribution is a new model, namely MSNet, which contains novel region proposal network designs.
arXiv Detail & Related papers (2020-06-30T02:23:05Z) - RescueNet: Joint Building Segmentation and Damage Assessment from
Satellite Imagery [83.49145695899388]
RescueNet is a unified model that can simultaneously segment buildings and assess the damage levels to individual buildings and can be trained end-to-end.
RescueNet is tested on the large scale and diverse xBD dataset and achieves significantly better building segmentation and damage classification performance than previous methods.
arXiv Detail & Related papers (2020-04-15T19:52:09Z) - Farmland Parcel Delineation Using Spatio-temporal Convolutional Networks [77.63950365605845]
Farm parcel delineation provides cadastral data that is important in developing and managing climate change policies.
This data can also be useful for the agricultural insurance sector for assessing compensations following damages associated with extreme weather events.
Using satellite imaging can be a scalable and cost effective manner to perform the task of farm parcel delineation.
arXiv Detail & Related papers (2020-04-11T19:49:09Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.